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from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
import cv2
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imgs_dir = ""
img_size = (1024,1024)
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def get_batches(path, gen=image.ImageDataGenerator(dim_ordering="th"), shuffle=True, batch_size=8, class_mode='categorical'):
return gen.flow_from_directory(path, target_size=img_size, class_mode=class_mode,
shuffle=shuffle, batch_size=batch_size, color_mode='grayscale')
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def load_image(filepath):
img = cv2.imread(filepath, cv2.IMREAD_COLOR)
return img
def load_data(images, img_size):
data = np.array([load_image(img, img_size).T for img in images])
return data
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img = load_image("")
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img.shape
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img.reshape(1, *img.shape).shape
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np.array([img]).shape
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X_batch, y_batch = next(get_batches(imgs_dir))
print(X_batch[0].shape)
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shift = 0.1
rotation_range = 90
zoom_range = 0.1
shear_range = 0.1
channel_shift_range = 20
horizontal_flip = True
vertical_flip = True
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imgen = image.ImageDataGenerator(rotation_range=rotation_range,
width_shift_range=shift, height_shift_range=shift,
zoom_range=zoom_range, shear_range=shear_range, channel_shift_range=channel_shift_range,
horizontal_flip=horizontal_flip, vertical_flip=vertical_flip)
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for i in range(2):
res = next(imgen.flow_from_directory(imgs_dir, target_size=img_size, classes=['car_ok'], class_mode='categorical', batch_size=10,
save_to_dir=imgs_dir))
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X_batch, y_batch = next(get_batches(imgs_dir, imgen))
print(X_batch[0].shape)
fig, axes = plt.subplots(len(X_batch))
for i, x in enumerate(X_batch):
axes[i].imshow(x.reshape(img_size), cmap=ptl.get_cmap('gray'))
plt.show()
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from skimage import data
from skimage import transform
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image = data.camera()
plt.imshow(image)
plt.show()
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plt.imshow(transform.rotate(image, 30, mode='edge'))
plt.show()
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